Random germs and stochastic watershed for unsupervised multispectral image segmentation

Authors:
Guillaume Noyel;Jesús Angulo;Dominique Jeulin
Affiliations:
Centre de Morphologie Mathématique, Ecole des Mines de Paris, Fontainebleau, France;Centre de Morphologie Mathématique, Ecole des Mines de Paris, Fontainebleau, France;Centre de Morphologie Mathématique, Ecole des Mines de Paris, Fontainebleau, France
Venue:
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Year:
2007

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Abstract

This paper extends the use of stochastic watershed, recently introduced by Angulo and Jeulin [1], to unsupervised segmentation of multispectral images. Several probability density functions (pdf), derived from Monte Carlo simulations (M realizations of N random markers), are used as a gradient for segmentation: a weighted marginal pdf a vectorial pdf and a probabilistic gradient. These gradient-like functions are then segmented by a volume-based watershed algorithm to define the R largest regions. The various gradients are computed in multispectral image space and in factor image space, which gives the best segmentation. Results are presented on PLEIADES satellite simulated images.